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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 65 Documents
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Search results for , issue "Vol 37, No 2: February 2025" : 65 Documents clear
Enhancing data cleaning process on accounting data for fraud detection Abdul Malek, Mohamad Affendi; Abd Jalil, Kamarularifin
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1014-1022

Abstract

Data cleaning is a crucial step in fraud detection as it involves identifying and correcting any inaccuracies or inconsistencies in the data. This can help to ensure that the data being used for fraud detection is reliable and accurate, which in turn can improve the effectiveness of fraud detection algorithms. Due to the overwhelming amount of data, data cleaning specific for fraud detection is a very important activity for the auditor to find the appropriate information. Therefore, a new accounting data cleaning for fraud detection is needed. In this paper, an enhancement of the process of fraud detection by accounting auditors through the implementation of accounting data cleaning technique is proposed. The proposed technique was embedded in a prototype system called accounting data cleaning for fraud detection (ADCFD). Through experiment, the performance of the proposed technique through ADCF is compared with those obtained from the IDEA system, using the same dataset. The results show that the proposed enhanced technique through ADCFD system performed better than the IDEA system.
Recognizing geographical locations using a GAN-based text-to-image approach Ibrahim, Dina M.; Al-Shargabi, Amal A.
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1168-1182

Abstract

Generating photo-realistic images that align with the text descriptions is the goal of the text-to-image generation (T2I) model. They can assist in visualizing the descriptions thanks to advancements in machine learning algorithms. Using text as a source, generative adversarial networks (GANs) can generate a series of pictures that serve as descriptions. Recent GANs have allowed oldest T2I models to achieve remarkable gains. However, they have some limitations. The main target of this study is to address these limitations to enhance the text-to-image generation models to enhance location services. To produce high-quality photos utilizing a multi-step approach, we build an attentional generating network called AttnGAN. The fine-grained image-text matching loss needed to train the AttnGAN’s generator is computed using our multimodal similarity model. With an inception score of 4.81 on the PatternNet dataset, our AttnGAN model achieves an impressive R-precision value of 70.61 percent. Because the PatternNet dataset comprises photographs, we’ve added verbal descriptions to each one to make it a text-based dataset instead. Many experiments have shown that AttnGAN’s proposed attention procedures, which are critical for text-to-image production in complex circumstances, are effective.
Optimization machine learning models for selecting transmit antennas in 5G/6G systems Ouldammar, Abdellah; Moulay Lakhdar, Abdelmounaim; Bouida, Ahmed; Merit, Khaled
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp819-828

Abstract

Transmit antenna selection (TAS) plays a crucial role in improving the performance and spectral efficiency of 5G/6G systems. This study proposes to use the GridSearchCV method for hyperparameter optimization in two supervised learning models, support vector machine (SVM) and K-nearest neighbors (KNN), to optimally select antenna peers based on channel gain. These models were applied to Alamouti’s space-time block coding to improve performance, resulting in increased signal-to-noise ratio (SNR) and reduced bit error rate (BER). The results show that optimizing the hyperparameters led to a significant improvement in the performance of the SVM and KNN models. The SVM and KNN models were evaluated using a variety of metrics, with the SVM demonstrating superior predictive performance in terms of accuracy, average macro recall, average macro precision, average macro F1 score, and cross-validation score. Even before optimization, the SVM outperforms the KNN in terms of performance metrics. After optimization, this gap widens further, demonstrating the robustness of SVM for classification tasks. Although KNN is faster to train.
SRCNN-based image transmission for autonomous vehicles in limited network areas Afina Carmelya, Anindya; Suryadi Satyawan, Arief; Muhammad Suranegara, Galura; Mirza Etnisa Haqiqi, Mokhamamad; Susilawati, Helfy; Alam Hamdani, Nizar; Dani Prasetyo Adi, Puput
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp903-912

Abstract

High-quality images are crucial for navigation, obstacle detection, and environmental understanding, but transmitting high-resolution images over constrained networks presents significant challenges. This study introduces an image transmission system using super-resolution convolutional neural networks (SRCNN) to enhance image quality without increasing bandwidth requirements by transmitting low-resolution images and upscaling them with SRCNN. The first phase of the research involved data collection, in which information was acquired directly from an appropriate locus to produce training, validation, and testing datasets. The second, three SRCNN models (915, 935, and 955) were trained using such a training dataset. The last was an evaluation, in which model 915 showed quick learning and stable performance with initial high loss, while model 935 had rapid convergence but potential overfitting. Model 955 achieved high initial performance. Three SRCNN model configurations were tailored to the specific needs of autonomous electric vehicles operating in limited areas, such as the locus. Input image resolution ranged from 128×128 pixels to 256×256 pixels, while output resolution varied from 256×256 pixels to 512×512 pixels. These resolutions can be acceptable for efficient image transmission over IEEE 802.11ac, but on the long range (LoRa) network, it still produces some delay.
Mobile application for distributing information to students at the Sciences and Humanities University Condori-Obregon, Patricia; Huallpa-Juarez, Carlos; Palomino-Vidal, Carlos
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1085-1092

Abstract

Currently, educational institutions around the world have implemented many standards and rules to ensure teaching quality. Many of these standards and rules are related to the use of technologies that provide students with services and facilities to learn. However, in Peru, a Latin American country, these standards and rules have been recently implemented, and as a result, information systems are required to guarantee teaching quality. This research exposes the implementation of a mobile application for distributing and managing information for students and teachers who require data about courses, grades, absences, and receive news about important university announcements. This work applied both research methods and Scrum methodologies together to demonstrate how the education process benefits from the use of technologies. As a result of these implementations, processes like finding academic information improved by an average of 50%. These results support that the implementation of mobile application technologies in educational environments is beneficial for guaranteeing process improvement and teaching quality.

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